Traffic Signal Violation Detection System - K. Pujitha,; J. Indu,; B. Sasi Vardhan,; P. Sandeep Kumar,; Mrs. G. Ramadevi, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Traffic signal violations are a major cause of accidents and traffic congestion. This project presents an automated Traffic Signal Violation Detection System using Deep Learning-based Object Detection. The system leverages SSD MobileNet V1, a pre-trained Convolutional Neural Network (CNN), to detect and classify traffic signals in real-time. Using the TensorFlow Object Detection API, the model identifies traffic lights and determines violations based on detected signals. The approach integrates image processing, real-time object detection, and violation recognition, providing an intelligent traffic monitoring solution. The proposed system enhances road safety, reduces human intervention, and supports smart city traffic management systems.
Keywords: Deep Learning; Object Detection; SSD MobileNet V1; Real-time Traffic Monitoring; CNN